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Results for “Discrete cosine transform”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Coding isotropic images

Rate distortion functions for two-dimensional homogeneous isotropic images are compared with the performance of 5 source encoders designed for such images. Both unweighted and frequency weighted mean square error distortion measures are considered. The coders considered are differential PCM (DPCM) using six previous samples in the prediction, herein called 6 pel (picutre element) DPCM; simple DPCM using single sample prediction; 6 pel DPCM followed by entropy coding; 8 x 8 discrete cosine transform coder, and 4 x 4 Hadamard transform coder. Other transform coders were studied and found to have about the same performance as the two transform coders above. With the mean square error distortion measure DPCM with entropy coding performed best. The relative performance of the coders changes slightly when the distortion measure is frequency weighted mean square error. The performance of all the coders was separated by only about 4 dB.

Oneal, J. B., Jr.↗
NASA NTRS1979

Design of a 24-channel transmultiplexer

The design of a transmultiplexer capable of performing the bilateral conversion between one 1544 kbit/s digital signal (which represents 24 PCM coded voice channels) and two analog group signals (each one containing 12 voice channels in the 60-108 kHz band) is investigated. It is shown that an FIR filter bank required as part of such a transmultiplexer can be realized efficiently by cascading a discrete cosine transform processor and a weighting network. Fast convolution algorithms are derived for evaluating the cosine transform. A method of using the symmetry conditions to reduce the computation rate in the weighting network and an elegant hardware configuration for implementing it are also discussed.

Narasimha, M. J.↗
NASA NTRS1988

Spatial compression of Seasat SAR imagery

The results of a study of techniques for spatial compression of synthetic-aperture-radar (SAR) imagery are summarized. Emphasis is on image-data volume reduction for archive and online storage applications while preserving the image resolution and radiometric fidelity. A quantitative analysis of various techniques, including vector quantization (VQ) and adaptive discrete cosine transform (ADCT), is presented. Various factors such as compression ratio, algorithm complexity, and image quality are considered in determining the optimal algorithm. The compression system requirements are established for electronic access of an online archive system based on the results of a survey of the science community. The various algorithms are presented and their results evaluated considering the effects of speckle noise and the wide dynamic range inherent in SAR imagery. The conclusion is that although the ADCT produces the best signal-to-distortion-noise ratio for a given compression ratio, the two-level tree-searched VQ technique is preferred due to its simplicity of decoding and near-optimal performance.

Chang, C. Y.↗
NASA NTRS1989

Psychophysical rating of image compression techniques

Image compression schemes abound with little work which compares their bit-rate performance based on subjective fidelity measures. Statistical measures of image fidelity, such as squared error measures, do not necessarily correspond to subjective measures of image fidelity. Most previous comparisons of compression techniques have been based on these statistical measures. A psychophysical method has been used to estimate, for a number of compression techniques, a threshold bit-rate yielding a criterion level of performance in discriminating original and compressed images. The compression techniques studied include block truncation, Laplacian pyramid, block discrete cosine transform, with and without a human visual system scaling, and cortex transform coders.

Stein, Charles S.↗
NASA NTRS1991

Compressed/reconstructed test images for CRAF/Cassini

A set of compressed, then reconstructed, test images submitted to the Comet Rendezvous Asteroid Flyby (CRAF)/Cassini project is presented as part of its evaluation of near lossless high compression algorithms for representing image data. A total of seven test image files were provided by the project. The seven test images were compressed, then reconstructed with high quality (root mean square error of approximately one or two gray levels on an 8 bit gray scale), using discrete cosine transforms or Hadamard transforms and efficient entropy coders. The resulting compression ratios varied from about 2:1 to about 10:1, depending on the activity or randomness in the source image. This was accomplished without any special effort to optimize the quantizer or to introduce special postprocessing to filter the reconstruction errors. A more complete set of measurements, showing the relative performance of the compression algorithms over a wide range of compression ratios and reconstruction errors, shows that additional compression is possible at a small sacrifice in fidelity.

Dolinar, S.↗
NASA NTRS1991

SAR data compression: Application, requirements, and designs

The feasibility of reducing data volume and data rate is evaluated for the Earth Observing System (EOS) Synthetic Aperture Radar (SAR). All elements of data stream from the sensor downlink data stream to electronic delivery of browse data products are explored. The factors influencing design of a data compression system are analyzed, including the signal data characteristics, the image quality requirements, and the throughput requirements. The conclusion is that little or no reduction can be achieved in the raw signal data using traditional data compression techniques (e.g., vector quantization, adaptive discrete cosine transform) due to the induced phase errors in the output image. However, after image formation, a number of techniques are effective for data compression.

Curlander, John C.↗
NASA NTRS1992

Modeling of video traffic in packet networks, low rate video compression, and the development of a lossy+lossless image compression algorithm

During this reporting period we have worked on three somewhat different problems. These are modeling of video traffic in packet networks, low rate video compression, and the development of a lossy + lossless image compression algorithm, which might have some application in browsing algorithms. The lossy + lossless scheme is an extension of work previously done under this grant. It provides a simple technique for incorporating browsing capability. The low rate coding scheme is also a simple variation on the standard discrete cosine transform (DCT) coding approach. In spite of its simplicity, the approach provides surprisingly high quality reconstructions. The modeling approach is borrowed from the speech recognition literature, and seems to be promising in that it provides a simple way of obtaining an idea about the second order behavior of a particular coding scheme. Details about these are presented.

Sayood, K.↗
NASA NTRS1993

Compression of spectral meteorological imagery

Data compression is essential to current low-earth-orbit spectral sensors with global coverage, e.g., meteorological sensors. Such sensors routinely produce in excess of 30 Gb of data per orbit (over 4 Mb/s for about 110 min) while typically limited to less than 10 Gb of downlink capacity per orbit (15 minutes at 10 Mb/s). Astro-Space Division develops spaceborne compression systems for compression ratios from as little as three to as much as twenty-to-one for high-fidelity reconstructions. Current hardware production and development at Astro-Space Division focuses on discrete cosine transform (DCT) systems implemented with the GE PFFT chip, a 32x32 2D-DCT engine. Spectral relations in the data are exploited through block mean extraction followed by orthonormal transformation. The transformation produces blocks with spatial correlation that are suitable for further compression with any block-oriented spatial compression system, e.g., Astro-Space Division's Laplacian modeler and analytic encoder of DCT coefficients.

Miettinen, Kristo↗
NASA NTRS1992

Luminance-model-based DCT quantization for color image compression

A model is developed to approximate visibility thresholds for discrete cosine transform (DCT) coefficient quantization error based on the peak-to-peak luminance of the error image. Experimentally measured visibility thresholds for R, G, and B DCT basis functions can be predicted by a simple luminance-based detection model. This model allows DCT coefficient quantization matrices to be designed for display conditions other than those of the experimental measurements: other display luminances, other veiling luminances, and other spatial frequencies (different pixel spacings, viewing distances, and aspect ratios).

Ahumada, Albert J., Jr.↗
NASA NTRS1994

A Motion-Compensating Image-Compression Scheme

Chrominance used (in addition to luminance) in estimating motion. Variable-rate digital coding scheme for compression of color-video-image data designed to deliver pictures of good quality at moderate compressed-data rate of 1 to 2 bits per pixel, or of fair quality at rate less than 1 bit per pixel. Scheme, in principle, implemented by use of commercially available application-specific integrated circuits. Incorporates elements of some prior coding schemes, including motion compensation (MC) and discrete cosine transform (DCT).

Wong, Carol↗
NASA NTRS1992

A VLSI implementation of DCT using pass transistor technology

A VLSI design for performing the Discrete Cosine Transform (DCT) operation on image blocks of size 16 x 16 in a real time fashion operating at 34 MHz (worst case) is presented. The process used was Hewlett-Packard's CMOS26--A 3 metal CMOS process with a minimum feature size of 0.75 micron. The design is based on Multiply-Accumulate (MAC) cells which make use of a modified Booth recoding algorithm for performing multiplication. The design of these cells is straight forward, and the layouts are regular with no complex routing. Two versions of these MAC cells were designed and their layouts completed. Both versions were simulated using SPICE to estimate their performance. One version is slightly faster at the cost of larger silicon area and higher power consumption. An improvement in speed of almost 20 percent is achieved after several iterations of simulation and re-sizing.

Kamath, S.↗
NASA NTRS1994

Image-adapted visually weighted quantization matrices for digital image compression

A method for performing image compression that eliminates redundant and invisible image components is presented. The image compression uses a Discrete Cosine Transform (DCT) and each DCT coefficient yielded by the transform is quantized by an entry in a quantization matrix which determines the perceived image quality and the bit rate of the image being compressed. The present invention adapts or customizes the quantization matrix to the image being compressed. The quantization matrix comprises visual masking by luminance and contrast techniques and by an error pooling technique all resulting in a minimum perceptual error for any given bit rate, or minimum bit rate for a given perceptual error.

Watson, Andrew B.↗
NASA NTRS1994

A visual detection model for DCT coefficient quantization

The discrete cosine transform (DCT) is widely used in image compression and is part of the JPEG and MPEG compression standards. The degree of compression and the amount of distortion in the decompressed image are controlled by the quantization of the transform coefficients. The standards do not specify how the DCT coefficients should be quantized. One approach is to set the quantization level for each coefficient so that the quantization error is near the threshold of visibility. Results from previous work are combined to form the current best detection model for DCT coefficient quantization noise. This model predicts sensitivity as a function of display parameters, enabling quantization matrices to be designed for display situations varying in luminance, veiling light, and spatial frequency related conditions (pixel size, viewing distance, and aspect ratio). It also allows arbitrary color space directions for the representation of color. A model-based method of optimizing the quantization matrix for an individual image was developed. The model described above provides visual thresholds for each DCT frequency. These thresholds are adjusted within each block for visual light adaptation and contrast masking. For given quantization matrix, the DCT quantization errors are scaled by the adjusted thresholds to yield perceptual errors. These errors are pooled nonlinearly over the image to yield total perceptual error. With this model one may estimate the quantization matrix for a particular image that yields minimum bit rate for a given total perceptual error, or minimum perceptual error for a given bit rate. Custom matrices for a number of images show clear improvement over image-independent matrices. Custom matrices are compatible with the JPEG standard, which requires transmission of the quantization matrix.

Ahumada, Albert J., Jr.↗
NASA NTRS1993

The importance of robust error control in data compression applications

Data compression has become an increasingly popular option as advances in information technology have placed further demands on data storage capabilities. With compression ratios as high as 100:1 the benefits are clear; however, the inherent intolerance of many compression formats to error events should be given careful consideration. If we consider that efficiently compressed data will ideally contain no redundancy, then the introduction of a channel error must result in a change of understanding from that of the original source. While the prefix property of codes such as Huffman enables resynchronisation, this is not sufficient to arrest propagating errors in an adaptive environment. Arithmetic, Lempel-Ziv, discrete cosine transform (DCT) and fractal methods are similarly prone to error propagating behaviors. It is, therefore, essential that compression implementations provide sufficient combatant error control in order to maintain data integrity. Ideally, this control should be derived from a full understanding of the prevailing error mechanisms and their interaction with both the system configuration and the compression schemes in use.

Woolley, S. I.↗
NASA NTRS1993

Visual optimization of DCT quantization matrices for individual images

Many image compression standards (JPEG, MPEG, H.261) are based on the Discrete Cosine Transform (DCT). However, these standards do not specify the actual DCT quantization matrix. We have previously provided mathematical formulae to compute a perceptually lossless quantization matrix. Here I show how to compute a matrix that is optimized for a particular image. The method treats each DCT coefficient as an approximation to the local response of a visual 'channel'. For a given quantization matrix, the DCT quantization errors are adjusted by contrast sensitivity, light adaptation, and contrast masking, and are pooled non-linearly over the blocks of the image. This yields an 8x8 'perceptual error matrix'. A second non-linear pooling over the perceptual error matrix yields total perceptual error. With this model we may estimate the quantization matrix for a particular image that yields minimum bit rate for a given total perceptual error, or minimum perceptual error for a given bit rate. Custom matrices for a number of images show clear improvement over image-independent matrices. Custom matrices are compatible with the JPEG standard, which requires transmission of the quantization matrix.

Watson, Andrew B.↗
NASA NTRS1993

A visual detection model for DCT coefficient quantization

The discrete cosine transform (DCT) is widely used in image compression, and is part of the JPEG and MPEG compression standards. The degree of compression, and the amount of distortion in the decompressed image are determined by the quantization of the transform coefficients. The standards do not specify how the DCT coefficients should be quantized. Our approach is to set the quantization level for each coefficient so that the quantization error is at the threshold of visibility. Here we combine results from our previous work to form our current best detection model for DCT coefficient quantization noise. This model predicts sensitivity as a function of display parameters, enabling quantization matrices to be designed for display situations varying in luminance, veiling light, and spatial frequency related conditions (pixel size, viewing distance, and aspect ratio). It also allows arbitrary color space directions for the representation of color.

Ahumada, Albert J., Jr.↗
NASA NTRS1994

Performance of customized DCT quantization tables on scientific data

We show that it is desirable to use data-specific or customized quantization tables for scaling the spatial frequency coefficients obtained using the Discrete Cosine Transform (DCT). DCT is widely used for image and video compression (MP89, PM93) but applications typically use default quantization matrices. Using actual scientific data gathered from divers sources such as spacecrafts and electron-microscopes, we show that the default compression/quality tradeoffs can be significantly improved upon by using customized tables. We also show that significant improvements are possible for the standard test images Lena and Baboon. This work is part of an effort to develop a practical scheme for optimizing quantization matrices for any given image or video stream, under any given quality or compression constraints.

Ratnakar, Viresh↗
NASA NTRS1994

Fast computational scheme of image compression for 32-bit microprocessors

This paper presents a new computational scheme of image compression based on the discrete cosine transform (DCT), underlying JPEG and MPEG International Standards. The algorithm for the 2-d DCT computation uses integer operations (register shifts and additions / subtractions only); its computational complexity is about 8 additions per image pixel. As a meaningful example of an on-board image compression application we consider the software implementation of the algorithm for the Mars Rover (Marsokhod, in Russian) imaging system being developed as a part of Mars-96 International Space Project. It's shown that fast software solution for 32-bit microprocessors may compete with the DCT-based image compression hardware.

Kasperovich, Leonid↗